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Data-driven heterogeneity in mathematical learning disabilities based on the triple code model
Christian Peake1, Juan E Jiménez2, Cristina Rodríguez3
1Faculty of Education, Universidad Católica de la Santísima Concepción, Concepción, Chile; Centro de Investigación en Educación y Desarrollo, CIEDE-UCSC, Concepción, Chile.
Research in Developmental Disabilities
|October 17, 2017
Summary
This study empirically tested the Triple Code Model's (TCM) postulates for mathematical learning disabilities (MLD). Findings reveal distinct representational and number-fact retrieval subtypes of MLD in children.
Area of Science:
- Cognitive Psychology
- Developmental Neuroscience
- Educational Psychology
Background:
- Mathematical learning disabilities (MLD) are complex, with numerous proposed classifications lacking empirical validation.
- The Triple Code Model (TCM) posits MLD as heterogeneous, suggesting representational and verbal subtypes.
Purpose of the Study:
- To empirically investigate the heterogeneity of MLD based on the TCM's postulates.
- To identify distinct cognitive profiles within MLD using data-driven approaches.
Main Methods:
- A sample of 3rd to 6th graders with MLD was analyzed.
- Data-driven clustering strategies were employed, focusing on cognitive variables predicted by the TCM.
- Participants were divided into younger (3rd-4th grade) and older (5th-6th grade) cohorts.
Main Results:
- Children with MLD clustered into groups with representational deficits and number-fact retrieval deficits, aligning with TCM predictions.
- A spatial subtype emerged in the younger cohort.
- A non-specific cluster, unexplained by the TCM, was identified in both cohorts.
Conclusions:
- The TCM provides a valid framework for understanding MLD heterogeneity, identifying key subtypes.
- MLD presents with distinct cognitive profiles, including representational and number-fact retrieval deficits.
- Further research is needed to explain the non-specific MLD cluster.